Minimum Detectable Rate Research Articles

Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images.

COVID-19 pandemic has a significant impact on the global health and daily lives of people living over the globe. Several initial tests are based on the detecting of the genetic material of the coronavirus, and they have a minimum detection rate with a time-consuming process. To overcome this issue, radiological images are recommended where chest X-rays (CXRs) are employed in the diagnostic process. This article introduces a new Multi-modal fusion of deep transfer learning (MMF-DTL) technique to classify COVID-19. The proposed MMF-DTL model involves three main processes, namely pre-processing, feature extraction, and classification. The MMF-DTL model uses three DL models namely VGG16, Inception v3, and ResNet 50 for feature extraction. Since a single modality would not be adequate to attain an effective detection rate, the integration of three approaches by the use of decision-based multimodal fusion increases the detection rate. So, a fusion of three DL models takes place to further improve the detection rate. Finally, a softmax classifier is employed for test images to a set of six different. A wide range of experimental result analyses is carried out on the Chest-X-Ray dataset. The proposed fusion model is found to be an effective tool for COVID-19 diagnosis using radiological images with the average sensy of 92.96%, specy of 98.54%, precn of 93.60%, accuy of 98.80%, Fscore of 93.26% and kappa of 91.86%.

Protozoan and Rickettsial Pathogens in Ticks Collected from Infested Cattle from Turkey.

Diseases caused by tick-transmitted pathogens including bacteria, viruses, and protozoa are of veterinary and medical importance, especially in tropical and subtropical regions including Turkey. Hence, molecular surveillance of tick-borne diseases will improve the understanding of their distribution towards effective control. This study aimed to investigate the presence and perform molecular characterization of Babesia sp., Theileria sp., Anaplasma sp., Ehrlichia sp., and Rickettsia sp. in tick species collected from cattle in five provinces of Turkey. A total of 277 adult ticks (males and females) were collected. After microscopic identification, tick pools were generated according to tick species, host animal, and sampling sites prior to DNA extraction. Molecular identification of the tick species was conducted through PCR assays. Out of 90 DNA pools, 57.8% (52/90) were detected to harbor at least 1 pathogen. The most frequently-detected pathogens were Babesia bovis, with a minimum detection rate of 7.9%, followed by Ehrlichia sp. (7.2%), Theileria annulata (5.8%), Coxiella sp. (3.3%), Anaplasma marginale (2.5%), Rickettsia sp. (2.5%), and B. occultans (0.7%). Rickettsia sp. identified in this study include Candidatus Rickettsia barbariae, R. aeschlimannii, and Rickettsia sp. Chad. All sequences obtained from this study showed 99.05–100% nucleotide identity with those deposited in GenBank (query cover range: 89–100%). This is the first molecular detection of Rickettsia sp. Chad, a variant of Astrakhan fever rickettsia, in Turkey. Results from this survey provide a reference for the distribution of ticks and tick-borne pathogens in cattle and expand the knowledge of tick-borne diseases in Turkey.

Secure Visible Light Communication System via Cooperative Attack Detecting Techniques

With the recent development of fourth industrial technology, the need for a broadband short-range wireless communication system to realize an ultraconnected, ultra-low latency, and ultra-realistic intelligent information society has emerged. Among the next-generation communication network technologies that can fulfill the technical demands, visible light communication (VLC) is a promising technology that can use illuminated light as a communication light source, which is convenient and environmentally friendly and has high energy and frequency efficiency. However, although VLC has a high level of security owing to the straightness and transparency of visible light, if some of the VLC nodes in a dense mesh network environment are hacked by external attacks, there can be critical performance degradation by jamming attacks. Although several studies have suggested the possibility of VLC jamming attacks, only a few have studied how to effectively detect and respond to these attacks. This study proposes a method to collaboratively detect and respond to jamming attacks in smart LED-based VLC systems. According to the experimental results of this study, the proposed cooperative method showed 91% attack detection accuracy and 1.82 times better than the k-random method. The proposed method showed a minimum detection rate of 84% even in an obstacle-rich environment, proving outstanding attack detection performance 1.68 times better than the k-random method.

Blockchain and extreme learning machine based spectrum management in cognitive radio networks

AbstractIn recent times, spectrum sensing and spectrum management become a crucial design issue in cognitive radio networks (CRN). To improve the spectrum utilization in CRN, the secondary users (SUs) will try to utilize the spectrum resource when it is unoccupied by the authorized primary users (PUs). At the same time, blockchain principle has been introduced to efficiently identify the legitimate SUs and allocate the spectrum resource as per the demand specified by the SUs. In this view, this article presents a new machine learning (ML) with blockchain‐based spectrum management technique in CRN. The proposed model undergoes three processes, namely spectrum sensing, blockchain‐based spectrum access, and malicious user (MU) identification. Initially, ML‐based extreme learning machine (ELM) technique is applied for spectrum sensing. Then, the presented blockchain approach provides secured spectrum allocation for SUs. Finally, the MUs are identified and to be blocked from accessing the available spectrum resource. An extensive simulation analysis is carried out to ensure the goodness of the proposed model. The obtained results indicated that the proposed model has offered better performance compared with other methods. The experimental outcome stated that under the presence of −20 dB SNR, the proposed method has attained a maximum detection rate of 0.68, whereas the KNN and OR rule methods have demonstrated a minimum detection rate of 0.58 and 0.5, respectively.

Power spectra constrained IVA for SSVEP detection

The detection of steady state visual evoked potentials (SSVEPs), an evoked response to visual stimuli, has been identified as an effective solution for brain computer interface systems and as a probe for neurocognitive investigations of visually related tasks. However, since they recorded as part of the scalp-based electroencephalogram (EEG) signals their detection is challenging as they are buried amongst the normal brain signals. Blind source separation methods, such as independent vector analysis (IVA), have been shown to be capable of enhancing and improving signal detection by exploiting the diversity within individual datasets while simultaneously exploiting the complimentary information across datasets. In general, IVA is highly flexible with a general solution space; however, it is not guaranteed to converge to a meaningful minimum, by incorporating a problem specific constraint we can shrink the solution space insuring a relevant solution. In this work, we present a novel multiset data framework for EEG recordings and apply our constrained power spectra IVA (CP-IVA) to a publicly available SSVEP dataset. We compare the prediction accuracy of CP-IVA with that of an optimized processing stream developed for that dataset, as well as a canonical correlation analysis (CCA) based approach, showing that CP-IVA achieves better average performance and is more robust across the population of subjects with a higher minimum detection rate. More importantly, CP-IVA achieves this performance with minimal pre-processing and without the need to train complex classifiers.

Evaluation of primer detection capabilities of fimC Salmonella typhi using real time PCR for rapid detection of bacteria causes of food poisoning

Food poisoning is a disease caused by bacterial, viral or parasitic infections, which contaminate food. The purpose of this study was to obtain information about the detection of the primer pair of fimC Salmonella typhi genes using the Real-time PCR methods for the rapid kit development. The evaluation of the primer ability is determined by the accumulation of fluorescence signal from the amplification curve connecting the number of Cycle threshold (Ct) to the intensity of the amplicon signal that can reach the threshold line. The results showed that the primer fimC gene successfully amplifies the S. typhi DNA target fragment on Ct 14.783. In addition, the Ct data from this study also informed that the primer sensitivity of the fimC gene against the target S. typhi bacteria gave a minimum detection rate of 4,528 pg/μL, and the primer specificity evaluation of that primer to non-targeted bacteria Shigella dysenteriae gave Ct 27,949. Based on the data it can be concluded that fimC S. typhi primers gene can be used as sensitive and fast detection devices, but still require improvement in the specificity.

HIV Infection Among Potential Blood Donors

The statistics on HIV in Russia in 2013 were studied and compared with the blood service data. It was found that among different contingents the minimum detection rate of HIV infection has been registered among blood donors. Maximum detection rate of HIV among men, who have sex with men, actualizes the need for a ban on their participation in blood donation. The detection rate of HIV among donors, as well as the ratio of detection of HIV among donors and other categories surveyed indicate a lack of efficiency of formation of donor contingent of supporters of a healthy lifestyle. A direct positive correlation of HIV was detected in blood donors and region population, the volume of blood processing, as well as all volumes of blood wastage due to markers of blood borne infections except the volume due to HBs-antigen.

Effects of crack morphology parameters on leak-rate calculations in LBB evaluations

LBB analyses typically involve a two-step process. The first step is the determination of the through-wall crack length that leaks at the minimum detectable rate at normal operating conditions. In the second step, a safety factor is applied to this leakage detectable flaw length (i.e. the flaw length is increased by a factor) and then the stability of the resulting flaw at transient loads (i.e. seismic) is calculated. Probabilistic LBB analyses have shown that the leakage flaw size is more important to the failure probability than the fracture mechanics analysis at the seismic loads. The crack morphology is an important parameter in the calculation of the leakage flaw size. This paper contrasts results from using various crack morphology assumptions in leak-rate calculations. The results show that using a statistical analysis of surface roughness and numbers of turns from cracks removed from service can result in leakage crack sizes that are greatly different for different cracking mechanisms. Using improper morphology parameters can lead to large errors in leakage crack size and can produce non-conservative margins on the LBB critical crack size.

A novel micromachined vibrating rate-gyroscope with optical sensing and electrostatic actuation

This paper reports the fabrication and preliminary characterization of a novel vibratory rate-sensor. The sensor employs the modulated integrative differential optical sensing (MIDOS) to detect the output mode amplitude. The mechanical part of the sensor is fabricated by bulk micromachining using a double side anisotropic wet etching process. A CMOS chip containing the detecting photodiodes and their readout electronics is fabricated through MOSIS. Electrical and mechanical integration of the two parts is achieved by using the indium bumps technology. The proof mass is aligned with the detecting photodiodes, so that when at rest, equal portions of the two photodiodes are exposed. Several prototypes have been fabricated and tested on a rotating table. Good linearity (<1%) was shown on a −1000–1000°/s range and so far, a minimum detectable rate (MDR) below 1°/s was demonstrated. Future research will focus on lowering the MDR below 0.01°/s.

Characterization of a novel micromachined optical vibrating rate gyroscope

The present article presents experimental results measured on a new class of micromachined rate-gyroscope, incorporating integrated optical sensing. A hybrid cantilever type device was fabricated by bulk micromachining, bonded to a complementary metal-oxide-semiconductor chip and encapsulated in a vacuum package. The proof mass is electrostatically actuated and the mechanical output motion is sensed by modulated integrative differential optical sensing method. Characterization of the device, including measurements of the natural frequencies and angular rate, is reported. Noise measurements indicate noise equivalent rate of 10−2–10−3 deg/s/√Hz. Currently the measured minimum detectable rate is 1 deg/s due to experimental setup limitations.

Thermogravimetric analysis: temperature limits and rate of heating

In the case of the decomposition of carbonates and hydrates, the Polanyi-Wigner equation often applies. A mathematical expression relating the temperature at a given stage of the reaction to the rate of decomposition, fraction of reactant left, initial weight and area of the compound and finally the rate of heating has been derived from the above equation. For a given thermobalance the minimum detectable rate of decomposition and fraction of reactant left are fixed so that the observed final decomposition temperature is a function of the rate of heating for a constant weight and area of material. Using this equation, values for the final decomposition temperatures have been calculated and compared with data found in the literature for the decomposition of calcium carbonate and for the dehydration of potassium chrome alum and orthoboric acid. The equation also has been applied to the dehydration of 5-nitrobarbituric acid trihydrate as determined by us.